Best Paper Awards:
Sparsity Based Spectral Embedding: Application to Multi-Atlas Echocardiography Segmentation
Ozan Oktay, Wenzhe Shi, Jose Caballero, Kevin Keraudren, Daniel Rueckert
Predicting cross-task behavioral variables from fMRI data using the $k$-support norm
Michail Misyrlis, Anna B. Konova, Matthew B. Blaschko, Jean Honorio, Nelly Alia-Klein, Rita Z. Goldstein, Dimitris Samaras
Sparsity based compressive sensing and sparse learning have been widely investigated and applied in machine learning, computer vision, computer graphics and medical imaging. In the medical community, these methods have been used successfully to speed up MR scan time, MR image reconstruction, organ segmentation from CT and MRI and classification methods for diseases. Sparsity Techniques in Medical Imaging (STMI2014) is the second in a series of workshops on this topic in conjunction with MICCAI 2014. This workshop focuses on major trends and challenges in this area, and aims to identify cutting-edge research work with important potential impact in medical imaging.
Accepted papers will be encouraged to submit to a special issue of The International Journal of Computerized Medical Imaging and Graphics.
September 14th, 2014 8:00 - 12:00 at Boston, MA, USAKeynote Speakers
The goal of this workshop is to advance scientific research in sparse methods for medical imaging. It will foster dialogue and debate in this relatively new field which includes Compressive Sensing (CS), Sparse Learning (SL) and their applications to medical imaging. The technical program will consist of previously unpublished and invited papers, with substantial time allocated for discussion.
This workshop will include, but is not limited to the following topics on sparse methods: